Extension of no-reference deblurring methods through image fusion

Abstract

Extracting an optimal amount of information from a blurred image without a reference image for comparison is an pressing issue in image quality enhancement. Most studies have approached deblurring by using iterative algorithms in an attempt to deconvolve the blurred image into the ideal image. Deconvolution is difficult due to the need to estimate a point spread function for the blur after each iteration, which can be computationally expensive for many iterations which often causes some amount of distortion or "ringing" in the deblurred image. However, image fusion may provide a solution. By deblurring a no-reference image, then fusing it with the blurred image, it is possible to extract additional salient information from the fused image; however the deblurring process causes some degree of information loss. The act of fixing one section of the image can cause distortion in another section of the image. Hence, by fusing the blurred and deblurred images together, it is critical to retain important information from the blurred image and reduce the "ringing" in the deblurred image. To evaluate the fusion process, three different evaluation metrics are used: Mutual Information (MI), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). This paper details an extension of the no-reference image deblurring process and the initial results indicate that image fusion has the potential to be a useful tool in recovering information in a blurred image.